A Genomic-Based Approach Combining In Vivo Selection in Mice to Identify a Novel Virulence Gene in Leishmania
Bibliographic record
Abstract
BACKGROUND: Infection with Leishmania results in a broad spectrum of pathologies where L. infantum and L. donovani cause fatal visceral leishmaniasis and L. major causes destructive cutaneous lesions. The identification and characterization of Leishmania virulence genes may define the genetic basis for these different pathologies. METHODS AND FINDINGS: Comparison of the recently completed L. major and L. infantum genomes revealed a relatively small number of genes that are absent or present as pseudogenes in L. major and potentially encode proteins in L. infantum. To investigate the potential role of genetic differences between species in visceral infection, seven genes initially classified as absent in L. major but present in L. infantum were cloned from the closely related L. donovani genome and introduced into L. major. The transgenic L. major expressing the L. donovani genes were then introduced into BALB/c mice to select for parasites with increased virulence in the spleen to determine whether any of the L. donovani genes increased visceral infection levels. During the course of these experiments, one of the selected genes (LinJ32_V3.1040 (Li1040)) was reclassified as also present in the L. major genome. Interestingly, only the Li1040 gene significantly increased visceral infection in the L. major transfectants. The Li1040 gene encodes a protein containing a putative component of an endosomal protein sorting complex involved with protein transport. CONCLUSIONS: These observations demonstrate that the levels of expression and sequence variations in genes ubiquitously shared between Leishmania species have the potential to significantly influence virulence and tissue tropism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".